This software library offers enhanced tools for the pre-processing and analysis of structured data. Built on top of the Pandas library, this utility extends the functionality and enhances the efficiency of Pandas to streamline workflows. By enabling workflow automation and optimization, it aims to simplify and accelerate structured data pre-processing and analysis tasks. These optimized processes reduce manual effort, minimize human error, enhance consistency, and boost productivity for data scientists and analysts.
A few demos in PDF format are provided to offer a glimpse into the functionality of the software library. Access these demos on any machine with a PDF reader and review them offline, enjoying a reliable, consistent viewing experience without the hassle of layout issues.
This software is available under a dual-license model:
- Open-source: Licensed under the MIT License for non-commercial use, educational purposes, or personal projects.
- Commercial: For use in commercial products, services, or environments, a Commercial License is required.
See LICENSE for the MIT terms, and COMMERCIAL-LICENSE.md for commercial terms.
This software primarily relies on the following open-source libraries:
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Pandas: This library is used for data manipulation and analysis. It is licensed under the BSD 3-Clause License. You can find more information and the full license text at https://pandas.pydata.org.
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Scikit-Learn: This library is utilized for machine learning algorithms and tools. It is licensed under the BSD 3-Clause License. More information and the full license text can be found at https://scikit-learn.org.
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NumPy: This library is used for numerical computations and array processing. It is licensed under the BSD 3-Clause License. More information and the full license text can be found at https://numpy.org.
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Seaborn: This library provides advanced statistical data visualization tools based on Matplotlib. It is licensed under the BSD 3-Clause License. For more information, visit https://seaborn.pydata.org.
This package is developed independently and is not directly affiliated with or endorsed by these libraries.